1. Field of the Invention
The present invention relates to the field of predicting directional movements in the financial markets. More specifically, the present invention relates to methods and systems for devising and implementing automated artificial neural networks to predict market performance and directional movements of U.S. Treasury yields, mortgage option-adjusted spreads (OAS), interest rate swap spreads, and the U.S. Dollar/Mexican Peso exchange rate.
2. Background
Despite the assumptions of the efficient markets hypothesis (Fama, E., Efficient Capital Markets: A review of theory and empirical work, Journal of Finance, Vol. 25, pp. 383-417 (1970)), there is evidence that price changes in the U.S. Treasury market and other financial markets do not occur randomly. There are also claims that a technical model exists which can predict market direction at above-chance levels (Kean, J. A Trading System for T-Bond Futures. AI in Finance 1, 1994, 33-37; also, see T. Bass, The Predictors, Holt and Company, New York, 1999). Further, studies in behavioral finance consistently find that equity investors under react to market information on short time scales that cause markets to trend, and overreact to long-term trends that result in reversion to average price levels (Schleifer, A. Inefficient Markets: An Introduction to Behavioral Finance, Oxford University Press, 2000).